---
title: "Nutzungsstatistiken"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    source_code: embed
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE,
                      message = FALSE,
                      warning = FALSE,
                      options(scipen = 999))

library(flexdashboard)
library(readxl)
library(tidyverse)
library(lubridate)
library(gt)
library(janitor)
library(scales)
library(DT)
library(plotly)

# Ressources:
# https://towardsdatascience.com/building-an-hr-dashboard-in-r-using-flexdashboard-76d14ed3f32
# https://rmarkdown.rstudio.com/flexdashboard/using.html

```

Dash 2020 {data-icon="fa-globe"}
===================================== 

Row {data-width=150, data-height=200}
-----------------------------------------------------------------------

### Suchen in Primo 
(im Vergleich zum Vorjahr)

```{r}
articles <- "+16 %"
valueBox(articles, icon = "fa-search-plus", color = "green", href="#primo")
```

### Besuche der Website 
(im Vergleich zum Vorjahr)

```{r}
comments <- "+28 %"
valueBox(comments, icon = "fa-user-check", color = "green", href="#website")
```

### Ausleihe von Büchern 
(im Vergleich zum Vorjahr)

```{r}
spam <- "-35 %"
valueBox(spam, 
         icon = "fa-book",
         color = "red")
```

```{r eval=FALSE}
### 
#Die Zahl bezieht sich auf die prozentuale Veränderung zum Vorjahr 2019.
```


Row 
-----------------------------------------------------------------------


Primo {data-orientation=columns}
===================================== 

Column {data-width=400, data-height=400}
-----------------------------------------------------------------------

### Primo A

```{r}
Primo_Stat_Auswertungen <- read_excel("T:/Statistik/ALMA_ART/Primo_Stat_Auswertungen.xlsx", 
    sheet = "R_1", col_types = c("date", 
        "text", "numeric"))
```

```{r}
Primo <- Primo_Stat_Auswertungen %>%
  mutate(Year = year(Date))%>%
  group_by(Year, Action) %>%
  summarise(Value = sum(Value))
```

```{r}
Primo_3 <- Primo %>%
  summarise(Search_Total = sum(Value)) %>%
  mutate(Percent_Change = (Search_Total/(lag(Search_Total))-1))
```


```{r}
ggplot(Primo, aes(x = Year, y = Value)) +
  geom_col(aes(fill = Action), position = "dodge") +
  theme_classic() +
  labs(
    title = "Primo",
    subtitle = "Anzahl Suchen von 2018 bis 2020",
    fill = "Suchen",
    y = "",
    x = "Jahre"
  ) +
  theme(legend.position = "bottom")

#+
 # facet_wrap(~Date)
  
```



Column {data-width=200, data-height=200}  
-----------------------------------------------------------------------

### Primo B


```{r}
Primo_2 <- Primo %>%
spread(key = Year, value = Value) %>%
  adorn_totals("row")
```


```{r}
gt(Primo_3) %>%
  tab_header(
    title = md("Suchen in Primo"))
```



Website {data-orientation=columns}
===================================== 

Column {data-width=400}
-----------------------------------------------------------------------

### Chart A

```{r}
Website_Besuche <- read_excel("T:/Statistik/ALMA_ART/Website.xlsx", 
    sheet = "Besuche", col_types = c("numeric", 
        "text", "numeric"))
```

```{r}
Website_Besuche_2 <- Website_Besuche %>%
  group_by(Action) %>%
#  filter(Action == "Besuche") %>%
  mutate(Percent_Change = (Value/(lag(Value))-1))
```


```{r}
p1 <- ggplot(Website_Besuche, aes(x=Date, y = Value))+
  geom_col(aes(fill = Action), position = "dodge")+
  theme_classic()+
  labs(title = "Besuche und Seitenansichten 2018 bis 2020", fill = "", y = "", x = "")

ggplotly(p1)
```

### Chart B

```{r}
Website_OS <- read_excel("T:/Statistik/ALMA_ART/Website.xlsx", 
    sheet = "Desktop", col_types = c("text", "text", "numeric"))
```


```{r}
p1 <- Website_OS %>%
  filter(date == "2020") %>%
  plot_ly() %>% 
  add_pie(labels=Website_OS$action, values = Website_OS$value, hole = 0.6) %>%
  layout(title="Verwendung Betriebssyteme 2020")
p1
```



Column {data-width=400}
-----------------------------------------------------------------------


### Chart C

```{r}
Website_Users <- read_excel("T:/Statistik/ALMA_ART/Website.xlsx", 
    sheet = "Users", col_types = c("date", "numeric", "numeric"))
```

```{r}
Website_Users_2 <- Website_Users %>%
  mutate(month = as.factor(month(date, label = T, abb = T))) %>%
  mutate(year = as.factor(year(date)))

p2 <- ggplot(Website_Users_2, aes(x = month, y = visits, color = year))+
  geom_point(size=2)+
  geom_line(aes(x = month, y = visits, group = year), size = 1)+
  theme(axis.text.x = element_text(angle = 90))+
#  scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+
  theme_classic()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))+
  labs(title = "Besuche 2018 bis 2020", y = "Besuche", x = "", color = "")

ggplotly(p2)
```

### Chart D




```{r}
Website_Users_2 <- Website_Users %>%
  mutate(month = as.factor(month(date, label = T, abb = T))) %>%
  mutate(year = as.factor(year(date))) 
#%>%
#  filter(date >= "2019-01-01")

p1 <- ggplot(Website_Users_2, aes(x = month, y = unique_users, color = year))+
  geom_point(size=2)+
  geom_line(aes(x = month, y = unique_users, group = year), size = 1)+
  theme(axis.text.x = element_text(angle = 90))+
#  scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+
  theme_classic()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))+
  labs(title = "Unique Users 2018 bis 2020", y = "Unique Users", x = "", color = "")

ggplotly(p1)
```